Differential privacy has grown in popularity in recent years, as has the literature
exploring the intersection between statistics and DP. [1]} pointed out how a handful of common
robust statistical estimators could be extended to respect differential privacy.
[2]} compare DP mechanisms via convergence rates of distributions and densities
from DP releases and frame DP in statistical language more broadly. [3]} explores
model selection under DP, while [4]} explores model uncertainty.
[5]}, [6]}, [7]}, [8]}, and [9]}
propose methods for DP hypothesis testing in various domains.
[10]}, [11]}, [12]}, and [13]} all address the problem of
differentially private linear regression.
